| ID | comm | Domain | item | resp | dim_a | dim_r |
|---|---|---|---|---|---|---|
| 32 | G | P | Pa5 | 1 | 1 | 0 |
| 53 | G | S | Sa3 | 3 | 1 | 0 |
| 69 | G | S | Sa2 | 3 | 1 | 0 |
| 88 | G | P | Pa3 | 3 | 1 | 0 |
| 162 | N | P | Pa3 | 2 | 1 | 0 |
| 195 | N | S | Sr2 | 2 | 0 | 1 |
How to Measure Parents’ Developmental Foci: Domains and Methodologies
the University of Manchester
Sunday, the 1th of February, 2026
PhD in Education, The University of Manchester (2021–Present);
From belief to practice: Understanding teachers’ pedagogic choices in China’s mathematics education
MSc in Research Methods with Education (Distinction, 2020–2021);
The association between mathematics teachers’ exclusivity belief and pedagogic practice
MA in Mathematics and Pedagogy, The Education University of Hong Kong (2019–2020)
Cultural models of parenting and child development held by parents and others (Harkness & Super, 1996);
Also link to parents’ cultural belief systems/expectations/goals/focus etc.
“Within the constraints imposed by the wider environment, parents make choices about the best ways to take care of their children, and these choices tend to follow culturally recognizable patterns (Harkness & Super, 2020, p. 18).”
Individualism/collectivism (e.g., Triandis, 1989); distal/proximal parenting (Keller, 2009); autonomy/relatedness (Keller et al., 2006) …
+ : Easy to model (1|person);
- : Too simplified, criticisms argue autonomy and relatedness can coexist and need to coexist because they are both human needs (Keller, 2016; Oyserman et al., 2002).
Data from (Ndzenyuiy et al., 2026):
| Parental Ethnotheory Questionnaire (PEQ) Item | Factor Loading |
|---|---|
| It is important to rock a crying baby on the arms in order to console him/her. | 0.000 |
| Sleeping through the night should be trained as early as possible. | -0.209 |
| It is not necessary to react immediately to a crying baby. | -0.321 |
| You cannot start early enough to direct the infant’s attention towards objects and toys. | -0.152 |
| Gymnastics make a baby strong. | 0.507 |
| If a baby is fussy, he/she should be immediately picked up. | 0.767 |
| It is good for a baby to sleep alone. | 0.000 |
| When a baby cries, he/she should be narrated immediately. | 0.551 |
| Babies should be left crying for a moment in order to see whether they console themselves. | -0.175 |
| A baby should be always in close proximity with his/her mother, so that she can react immediately to his/her signals. | 0.424 |
For example, Kağıtçıbaşı’s (2017) four-field scheme with variations in the dimensions of agency (autonomy & heteronomy) and interpersonal distance (closeness & separateness).
How important is it that your child, when an adult (Liang et al., 2021)…
| Item | Item (stem continuation) | Dimension |
|---|---|---|
| 1 | … maintains good relationships with many people? | Relatedness |
| 2 | … cares about others’ feelings? | Relatedness |
| 3 | … is loyal to his or her friends? | Relatedness |
| 4 | … feels well connected to other people? | Relatedness |
| 5 | … is well connected to the extended family (grandparents, aunts, cousins, etc.)? | Relatedness |
| 6 | … tries to reach his or her goals without anyone else’s help? | Autonomy |
| 7 | … tries not to depend on someone else to achieve his or her goals? | Autonomy |
| 8 | … typically decides on a course of action without help from others? | Autonomy |
| 9 | … makes decisions about what to do without being influenced by others’ opinions? | Autonomy |
| 10 | … likes to live without many ties to others? | Separateness |
| 11 | … prefers to live alone? | Separateness |
| 12 | … keeps personal issues to himself or herself? | Separateness |
| 13 | … does things in traditional ways? | Heteronomy |
| 14 | … does the things that other people expect of him or her? | Heteronomy |
| 15 | … avoids doing things that other people say are wrong? | Heteronomy |
From (Kyllonen, 2015)
Comm in fixed/random effect)brms| ID | comm | Domain | item | resp | dim_a | dim_r |
|---|---|---|---|---|---|---|
| 32 | G | P | Pa5 | 1 | 1 | 0 |
| 53 | G | S | Sa3 | 3 | 1 | 0 |
| 69 | G | S | Sa2 | 3 | 1 | 0 |
| 88 | G | P | Pa3 | 3 | 1 | 0 |
| 162 | N | P | Pa3 | 2 | 1 | 0 |
| 195 | N | S | Sr2 | 2 | 0 | 1 |
fit <- brm(
formula = resp ~ 1 + comm + (0 + dim_a + dim_r | ID) + (1 | item),
data = simdata,
family = brmsfamily("acat", "logit"),
prior = c(
prior(normal(0, 3), class = "b"),
prior(normal(0, 1), class = "sd"),
prior(lkj(2), class = "cor")
),
backend = "cmdstanr", chains = 4, iter = 2000, warmup = 1000, cores = 4,
file = "models/fit_rsm"
) Family: acat
Links: mu = logit; disc = identity
Formula: resp ~ 1 + comm + (0 + dim_a + dim_r | ID) + (1 | item)
Data: simdata (Number of observations: 6000)
Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
total post-warmup draws = 4000
Multilevel Hyperparameters:
~ID (Number of levels: 200)
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sd(dim_a) 0.69 0.05 0.60 0.80 1.00 1426 2412
sd(dim_r) 0.68 0.05 0.58 0.79 1.00 1120 2257
cor(dim_a,dim_r) -0.29 0.08 -0.43 -0.13 1.00 876 1712
~item (Number of levels: 30)
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sd(Intercept) 0.72 0.10 0.55 0.93 1.01 791 1646
Regression Coefficients:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept[1] -0.91 0.15 -1.20 -0.61 1.01 347 590
Intercept[2] 0.07 0.15 -0.22 0.35 1.01 318 453
Intercept[3] 1.11 0.15 0.81 1.39 1.01 322 425
commN 0.60 0.08 0.44 0.77 1.00 1137 1867
Further Distributional Parameters:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
disc 1.00 0.00 1.00 1.00 NA NA NA
Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).
fit_sep <- brm(
formula = resp ~ 1 + comm + (0 + Domain:dim_a + Domain:dim_r | ID) + (1 | item),
data = simdata,
family = brmsfamily("acat", "logit"),
prior = c(
prior(normal(0, 1), class = "sd"),
prior(lkj(2), class = "cor")
),
chains = 4, iter = 2000, warmup = 1000, cores = 4, backend = "cmdstanr",
file = "models/fit_sep_dim"
) Family: acat
Links: mu = logit; disc = identity
Formula: resp ~ 1 + comm + (0 + Domain:dim_a + Domain:dim_r | ID) + (1 | item)
Data: simdata (Number of observations: 6000)
Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
total post-warmup draws = 4000
Multilevel Hyperparameters:
~ID (Number of levels: 200)
Estimate Est.Error l-95% CI u-95% CI Rhat
sd(DomainL:dim_a) 0.86 0.07 0.72 1.02 1.00
sd(DomainP:dim_a) 0.97 0.07 0.83 1.13 1.00
sd(DomainS:dim_a) 0.97 0.08 0.83 1.13 1.00
sd(DomainL:dim_r) 0.91 0.08 0.76 1.07 1.00
sd(DomainP:dim_r) 1.08 0.08 0.92 1.25 1.00
sd(DomainS:dim_r) 0.86 0.08 0.72 1.02 1.00
cor(DomainL:dim_a,DomainP:dim_a) 0.65 0.07 0.51 0.78 1.00
cor(DomainL:dim_a,DomainS:dim_a) 0.57 0.08 0.41 0.71 1.00
cor(DomainP:dim_a,DomainS:dim_a) 0.47 0.08 0.29 0.62 1.01
cor(DomainL:dim_a,DomainL:dim_r) -0.21 0.09 -0.39 -0.03 1.00
cor(DomainP:dim_a,DomainL:dim_r) -0.11 0.09 -0.29 0.08 1.01
cor(DomainS:dim_a,DomainL:dim_r) 0.06 0.10 -0.13 0.24 1.00
cor(DomainL:dim_a,DomainP:dim_r) -0.11 0.10 -0.30 0.08 1.00
cor(DomainP:dim_a,DomainP:dim_r) -0.61 0.07 -0.73 -0.46 1.00
cor(DomainS:dim_a,DomainP:dim_r) 0.03 0.09 -0.15 0.21 1.00
cor(DomainL:dim_r,DomainP:dim_r) 0.53 0.08 0.37 0.68 1.00
cor(DomainL:dim_a,DomainS:dim_r) -0.10 0.10 -0.29 0.10 1.00
cor(DomainP:dim_a,DomainS:dim_r) -0.10 0.10 -0.29 0.09 1.00
cor(DomainS:dim_a,DomainS:dim_r) -0.47 0.08 -0.62 -0.31 1.00
cor(DomainL:dim_r,DomainS:dim_r) 0.62 0.08 0.46 0.75 1.00
cor(DomainP:dim_r,DomainS:dim_r) 0.35 0.09 0.17 0.52 1.00
Bulk_ESS Tail_ESS
sd(DomainL:dim_a) 1637 2807
sd(DomainP:dim_a) 1513 2588
sd(DomainS:dim_a) 1841 2533
sd(DomainL:dim_r) 1992 2607
sd(DomainP:dim_r) 1734 2899
sd(DomainS:dim_r) 1626 2820
cor(DomainL:dim_a,DomainP:dim_a) 785 1539
cor(DomainL:dim_a,DomainS:dim_a) 898 1864
cor(DomainP:dim_a,DomainS:dim_a) 996 1846
cor(DomainL:dim_a,DomainL:dim_r) 749 1665
cor(DomainP:dim_a,DomainL:dim_r) 789 1749
cor(DomainS:dim_a,DomainL:dim_r) 1066 2013
cor(DomainL:dim_a,DomainP:dim_r) 796 1577
cor(DomainP:dim_a,DomainP:dim_r) 1353 2254
cor(DomainS:dim_a,DomainP:dim_r) 1541 2504
cor(DomainL:dim_r,DomainP:dim_r) 1857 3098
cor(DomainL:dim_a,DomainS:dim_r) 825 1572
cor(DomainP:dim_a,DomainS:dim_r) 951 1993
cor(DomainS:dim_a,DomainS:dim_r) 1166 2379
cor(DomainL:dim_r,DomainS:dim_r) 1996 2733
cor(DomainP:dim_r,DomainS:dim_r) 2076 2828
~item (Number of levels: 30)
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sd(Intercept) 0.83 0.12 0.63 1.11 1.00 641 1242
Regression Coefficients:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept[1] -1.13 0.17 -1.48 -0.81 1.01 331 682
Intercept[2] 0.06 0.16 -0.28 0.38 1.01 314 708
Intercept[3] 1.35 0.17 1.00 1.66 1.01 346 724
commN 0.70 0.09 0.52 0.87 1.00 989 1790
Further Distributional Parameters:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
disc 1.00 0.00 1.00 1.00 NA NA NA
Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).